TY - GEN A1 - Grübler, Lene Marie A1 - Müsgens, Felix T1 - Applying spatial decomposition in energy system models T2 - 20th International Conference on the European Energy Market (EEM) N2 - The European energy transition requires the expansion of renewable generators and consequently, the expansion of grid infrastructure and flexibility options. When modelling complex energy systems, the application of decomposition techniques is reasonable to keep models computationally tractable. Our paper focuses on the efficient optimization of energy systems covering large geographic areas. To accelerate the solving process, we apply a variant of Benders decomposition, which does not only apply temporal but also spatial decomposition. We show that by applying our decomposition approach, the solving time decreases up to 75% compared to the runtime of the equivalent monolithic model solved by a commercial solver and the barrier algorithm. Furthermore, we show that additionally applying spatial decomposition results in time savings compared to an only temporally decomposed approach. KW - Benders decomposition KW - Complexity reduction energy systems modeling KW - Grid expansion planning KW - Spatial decomposition Y1 - 2024 UR - https://ieeexplore.ieee.org/document/10608869 U6 - https://doi.org/10.1109/EEM60825.2024.10608869 SN - 2165-4093 SN - 2165-4077 VL - 2024 ER - TY - GEN A1 - Ben Amor, Souhir A1 - Möbius, Thonas A1 - Müsgens, Felix T1 - Bridging an energy system model with an ensemble deep-learning approach for electricity price forecasting T2 - General Economics (econ.GN) N2 - This paper combines a techno-economic energy system model with an econometric model to maximise electricity price forecasting accuracy. The proposed combination model is tested on the German day-ahead wholesale electricity market. Our paper also benchmarks the results against several econometric alternatives. Lastly, we demonstrate the economic value of improved price estimators maximising the revenue from an electric storage resource. The results demonstrate that our integrated model improves overall forecasting accuracy by 18 %, compared to available literature benchmarks. Furthermore, our robustness checks reveal that a) the Ensemble Deep Neural Network model performs best in our dataset and b) adding output from the techno-economic energy systems model as econometric model input improves the performance of all econometric models. The empirical relevance of the forecast improvement is confirmed by the results of the exemplary storage optimisation, in which the integration of the techno-economic energy syst Y1 - 2024 UR - https://arxiv.org/abs/2411.04880 U6 - https://doi.org/10.48550/arXiv.2411.04880 VL - 2024 SP - 1 EP - 49 ER - TY - GEN A1 - Sgarciu, Smaranda A1 - Müsgens, Felix A1 - Osorio, Sebastian A1 - Pahle, Michael T1 - Is Germany on track to achieve 2030 climate and energy targets? T2 - List Forum für Wirtschafts- und Finanzpolitik N2 - The future development of CO2emissions in the German electricity sector by 2030 is unclear: On the one hand, the amendment to the Climate Protection Act (from August 2021) aims to tighten emission reduction targets in order to promote the path to a decarbonised energy system. On the other hand, the complex interplay between the main instruments for reducing carbon emissions, fuel prices and the current energy shortage as a result of Russia's attack on Ukraine is creating pressure for an increase in emissions. In our study, we examine whether Germany is on track to achieve its climate protection targets for 2030 and which measures can increase the likelihood. To do this, we use an energy system model parameterized to reflect the situation in the energy market in 2021, i.e. before Russia's attack on Ukraine. In addition to the quantitative side, we provide a qualitative analysis of the energy market in the light of recent events. We stress the importance of introducing a carbon price floor that can be dynamically changed in response to the development of other market forces and policies. This instrument would institutionalize a more plausible path to decarbonization and provide reassurance to investors. Y1 - 2024 U6 - https://doi.org/10.1007/s41025-023-00255-0 SN - 2364-3943 VL - 49 SP - 93 EP - 107 ER - TY - GEN A1 - Hoffmann, Christin A1 - Byrukuri Gangadhar, Shanmukha Srinivas A1 - Müsgens, Felix T1 - Smells Like Green Energy - Quasi-Experimental Evidence on the Impact of Bioenergy Production on Residential Property Values T2 - SSRN eLibrary N2 - Residents' acceptance of bioenergy is a prerequisite for its fast and efficient development but is ambiguously discussed in practice and research. We interpret a causal impact of bioenergy plant commissioning on housing prices in their vicinity as the average net external effects for residents. We utilize bioenergy plant construction data in Germany between 2007 and 2022 as a quasi-experiment with naturally occurring control groups in their vicinity and apply recently improved difference-in-difference estimation procedures to analyze instantaneous and heterogeneous treatment effects. We find a significant and negative impact on housing prices if plants use gaseous biofuel, generate electricity on-site, and are medium-sized. The magnitude of the effect is comparable to those for solar fields. In contrast, we find no impact for small and large bioenergy plants and those that use solid or liquid biofuels. KW - Bioenergy KW - Local acceptance KW - differences-in-differences KW - Hedonic pricing Y1 - 2024 U6 - https://doi.org/10.2139/ssrn.4760312 SN - 1556-5068 SP - 1 EP - 26 ER - TY - GEN A1 - Sgarciu, Smaranda A1 - Scholz, Daniel A1 - Müsgens, Felix T1 - How CO2 prices accelerate decarbonisation – The case of coal-fired generation in Germany T2 - Energy Policy N2 - This paper analyses the potential impact of the world's two main coal phase-out instruments: 1) direct regulatory intervention restricting the operation of existing coal-fired generation capacity and prohibiting investment in new capacity and 2) market-based CO2-pricing instruments that make emission-intensive coal-fired generation less competitive. We quantify these instruments' potential effects in the empirical setting of Germany, where both instruments are employed concurrently. However, this paper's approach and methodology can be applied in any jurisdiction or energy system worldwide. Our paper provides quantitative results with a pan-European partial-equilibrium electricity system model. The model applies an innovative three-step approach. Step 1 solves an investment and dispatch problem with reduced technical and temporal complexity for European countries. Step 2 computes the dispatch problem at a bihourly resolution and step 3 solves the investment and dispatch problem for Germany with increased technical complexity. Our results confirm that both a regulated phase-out as well as a price on CO2 emissions can reduce the amount of coal-fired generation in an electricity system. If CO2 prices remain at current levels, coal-fired power plants leave the market significantly before the regulated phase-out date, reducing demand for employees in the Lusatian lignite industry. Regions and federal governments should take this finding into account when planning and preparing for structural change. KW - Kohleausstieg KW - Coal phase-out KW - CO2 price KW - Employment KW - Decarbonisation KW - Energy systems modelling Y1 - 2023 U6 - https://doi.org/10.1016/j.enpol.2022.113375 SN - 0301-4215 VL - 173 ER - TY - GEN A1 - Genge, Lucien A1 - Scheller, Fabian A1 - Müsgens, Felix T1 - Supply costs of green chemical energy carriers at the European border: A meta-analysis T2 - International Journal of Hydrogen Energy N2 - Importing green chemical energy carriers is crucial for meeting European climate targets. However, estimating the costs of supplying these energy carriers to Europe remains challenging, leading to a wide range of reported supply-cost estimates. This study analyzes the estimated supply costs of green chemical energy carriers at the European border using a dataset of 1050 data points from 30 studies. The results reveal significant variations in supply costs, with a projected four-fold difference in 2030 and a five-fold difference in 2050 across all energy carriers. The main drivers of cost differences are varying production costs, particularly influenced by the weighted average costs of capital and capital expenditures of renewable energy sources, electrolyzers, and carrier-specific conversion processes. Transport costs also contribute to variations, mainly influenced by the choice of energy carrier and the weighted average costs of capital. To optimize cost-efficiency and sustainability in the chemical energy carrier sector, this paper recommends prioritizing transparency and sensitivity analyses of key input parameters, classifying energy carriers based on technological and economic status, and encouraging research and development to reduce production costs. KW - Green chemical energy carriers KW - Hydrogen derivates KW - Hydrogen supply costs KW - Hydrogen production costs KW - Hydrogen transportation costs KW - Meta-analysis Y1 - 2023 U6 - https://doi.org/10.1016/j.ijhydene.2023.06.180 SN - 0360-3199 VL - 48 IS - 98 SP - 38766 EP - 38781 ER - TY - GEN A1 - Watermeyer, Mira A1 - Möbius, Thomas A1 - Grothe, Oliver A1 - Müsgens, Felix T1 - A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling T2 - arXiv N2 - The accurate prediction of short-term electricity prices is vital for effective trading strategies, power plant scheduling, profit maximisation and efficient system operation. However, uncertainties in supply and demand make such predictions challenging. We propose a hybrid model that combines a techno-economic energy system model with stochastic models to address this challenge. The techno-economic model in our hybrid approach provides a deep understanding of the market. It captures the underlying factors and their impacts on electricity prices, which is impossible with statistical models alone. The statistical models incorporate non-techno-economic aspects, such as the expectations and speculative behaviour of market participants, through the interpretation of prices. The hybrid model generates both conventional point predictions and probabilistic forecasts, providing a comprehensive understanding of the market landscape. Probabilistic forecasts are particularly valuable because they account for market uncertainty, facilitating informed decision-making and risk management. Our model delivers state-of-the-art results, helping market participants to make informed decisions and operate their systems more efficiently. Y1 - 2023 U6 - https://doi.org/10.48550/arXiv.2304.09336 SP - 1 EP - 38 ER - TY - GEN A1 - Möbius, Thomas A1 - Watermeyer, Mira A1 - Grothe, Oliver A1 - Müsgens, Felix T1 - Enhancing energy system models using better load forecasts T2 - Energy Systems N2 - Since energy system models require a large amount of technical and economic data, their quality significantly affects the reliability of the results. However, some publicly available data sets, such as the transmission system operators’ day-ahead load forecasts, are known to be biased and inaccurate, leading to lower energy system model performance. We propose a time series model that enhances the accuracy of transmission system operators’ load forecast data in real-time, using only the load forecast error’s history as input. We further present an energy system model developed specifically for price forecasts of the short-term day-ahead market. We demonstrate the effectiveness of the improved load data as input by applying it to this model, which shows a strong reduction in pricing errors, particularly during periods of high prices and tight markets. Our results highlight the potential of our method the enhance the accuracy of energy system models using improved input data. KW - Data pre-processing KW - Day-ahead electricity prices KW - Energy system modelling Y1 - 2023 U6 - https://doi.org/10.1007/s12667-023-00590-3 SN - 1868-3975 SP - 1 EP - 30 ER - TY - GEN A1 - Batz Liñeiro, Taimyra A1 - Müsgens, Felix T1 - Evaluating the German onshore wind auction programme: An analysis based on individual bids T2 - Energy Policy N2 - Auctions are a highly demanded policy instrument for the promotion of renewable energy sources. Their flexible structure makes them adaptable to country-specific conditions and needs. However, their success depends greatly on how those needs are operationalised in the design elements. Disaggregating data from the German onshore wind auction programme into individual projects, we evaluated the contribution of auctions to the achievement of their primary (deployment at competitive prices) and secondary (diversity) objectives and have highlighted design elements that affect the policy's success or failure. We have shown that, in the German case, the auction scheme is unable to promote wind deployment at competitive prices, and that the design elements used to promote the secondary objectives not only fall short at achieving their intended goals, but create incentives for large actors to game the system. KW - Renewable energy KW - Auction KW - Wind KW - Onshore KW - Community energy KW - companies KW - Germany Y1 - 2023 U6 - https://doi.org/10.1016/j.enpol.2022.113317 SN - 1873-6777 SN - 0301-4215 VL - 172 ER - TY - GEN A1 - Möbius, Thomas A1 - Riepin, Iegor A1 - Müsgens, Felix A1 - van der Weijde, Adriaan H. T1 - Risk aversion and flexibility options in electricity markets T2 - Energy Economics N2 - Investments in electricity transmission and generation capacity must be made despite significant uncertainty about the future developments. The sources of this uncertainty include, among others, the future levels and spatiotemporal distribution of electricity demand, fuel costs and future energy policy. In recent years, these uncertainties have increased due to the ongoing evolution of supply- and demand-side technologies and rapid policy changes designed to encourage a transition to low-carbon energy systems. Because transmission and generation investments have long lead times and are difficult to reverse, they are subject to a considerable – and arguably growing – amount of risk. KW - Flexibility KW - Storage KW - Demand response KW - Generation and transmission KW - expansion KW - Investment KW - Risk aversion KW - Stochastic programming Y1 - 2023 SN - 0140-9883 SN - 1873-6181 VL - 126 ER - TY - GEN A1 - Hoffmann, Christin A1 - Ziemann, Niklas A1 - Penske, Franziska A1 - Müsgens, Felix T1 - The value of secure electricity supply for increasing acceptance of green hydrogen - First experimental evidence from the virtual reality lab T2 - 19th International Conference on the European Energy Market (EEM), 06-08 June 2023, Lappeenranta, Finland N2 - To unlock the high potential of green hydrogen in reaching the ambitious 1.5 °C goal declared by the Paris Agreement, enormous (public) investments are needed. Social acceptance of these investments is required in order to implement hydrogen technologies as fast and efficiently as possible. This study investigates which benefits associated with green hydrogen foster its social acceptance. Using a between-subject design, we implement two different treatments. Both treatments have in common that the participants experience the transformation into a hydrogen economy in a virtual reality scenario. In "Info Security of Supply", we provide the participants information about the benefits of green hydrogen regarding the security of energy supply and climate protection. In Control, we inform them only about the benefits of climate protection. Subsequently, the participants decide about the financial support of a hydrogen project. Our preliminary results show a higher support if the focus is solely on the positive impact of green hydrogen for climate protection. Y1 - 2023 UR - https://ieeexplore.ieee.org/abstract/document/10161844 SN - 979-8-3503-1258-4 SN - 979-8-3503-2452-5 U6 - https://doi.org/10.1109/EEM58374.2023.10161844 SN - 2165-4093 ER - TY - GEN A1 - Riepin, Iegor A1 - Sgarciu, Smaranda A1 - Bernecker, Maximilian A1 - Möbius, Thomas A1 - Müsgens, Felix T1 - Grok It and Use It: Teaching Energy Systems Modeling T2 - SSRN eLibrary N2 - This article details our experience developing and teaching an “Energy Systems Modeling” course, which sought to introduce graduate-level students to operations research, energy economics, and system modeling using the General Algebraic Modeling System (GAMS). In this paper, we focus on (i) the mathematical problems discussed in the course, (ii) the energy-related empirical interpretations of these mathematical problems, and (iii) the best teaching practices (i.e., our experiences regarding how to make the content interesting and accessible for students). KW - Energy Systems KW - Mathematical Programming KW - Optimization KW - Teaching Y1 - 2023 U6 - https://doi.org/10.2139/ssrn.4320978 SN - 1556-5068 ER -